Lily Hu

About

Lily Hu is an Assistant Professor of Philosophy at Yale University. Her current research projects broadly concern causal theorizing about the social world, with a particular focus on causal inference methodologies in the social sciences. She investigates how various statistical frameworks treat and measure the "causal effect" of social categories such as race, and how these methods are seen to support normative claims about racial discrimination and inequalities more broadly. Previously, she worked on topics in machine learning theory and algorithmic fairness. In addition to her academic work, Lily Hu is a contributing editor at Boston Review and has written for other venues including the Los Angeles Review of Books, Phenomenal World, and The Law & Political Economy Project. She received her doctorate from Harvard University in 2022 and also completed her undergraduate degree in Mathematics at Harvard.

Research topics

• Computer Science
• Political Science
• Artificial Intelligence
• Law
• Sociology
• Machine Learning
• Mathematics
• Environmental ethics
• Mathematical economics
• Economics

Selected publications

• What is “Race” in Algorithmic Discrimination on the Basis of Race?

  Journal of Moral Philosophy · 2023-09-05 · 24 citations

  article1st authorCorresponding

  Abstract Machine learning algorithms bring out an under-appreciated puzzle of discrimination, namely figuring out when a decision made on the basis of a factor correlated with race is a decision made on the basis of race . I argue that prevailing approaches, which are based on identifying and then distinguishing among causal effects of race, in their metaphysical timidity, fail to get off the ground. I suggest, instead, that adopting a constructivist theory of race answers this puzzle in a princ…

  PublisherDOI

• Embedded EthiCS: Integrating Ethics Broadly Across Computer Science Education

  London School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2018-08-16 · 13 citations

  preprintOpen access

  Computing technologies have become pervasive in daily life, sometimes bringing unintended but harmful consequences. For students to learn to think not only about what technology they could create, but also about what technology they should create, computer science curricula must expand to include ethical reasoning about the societal value and impact of these technologies. This paper presents Embedded EthiCS, a novel approach to integrating ethics into computer science education that incorporates…

  PublisherOA PDFDOI

• Embedded EthiCS: Integrating Ethics Broadly Across Computer Science\n Education

  arXiv (Cornell University) · 2018-08-16 · 9 citations

  preprintOpen access

  Computing technologies have become pervasive in daily life, sometimes\nbringing unintended but harmful consequences. For students to learn to think\nnot only about what technology they could create, but also about what\ntechnology they should create, computer science curricula must expand to\ninclude ethical reasoning about the societal value and impact of these\ntechnologies. This paper presents Embedded EthiCS, a novel approach to\nintegrating ethics into computer science education that incorp…

  PublisherOA PDFDOI

• Fair Classification and Social Welfare

  arXiv (Cornell University) · 2019-05-01 · 4 citations

  preprintOpen access1st authorCorresponding

  Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of social welfare? In this paper, we present a welfare-based analysis of classification and fairness regimes. We translate a loss minimization program into a social welfare maximization…

  PublisherOA PDFDOI

• What Is Perceived When Race Is Perceived and Why It Matters for Causal Inference and Discrimination Studies

  Law & Society Review · 2024-12-05 · 3 citations

  articleOpen access1st authorCorresponding

  Abstract Quantifying the causal effects of race is one of the more controversial and consequential endeavors to have emerged from the causal revolution in the social sciences. The predominant view within the causal inference literature defines the effect of race as the effect of race perception and commonly equates this effect with “disparate treatment” racial discrimination. If these concepts are indeed equivalent, the stakes of these studies are incredibly high as they stand to establish or di…

  PublisherOA PDFDOI

Frequent coauthors

• Dennis F. Deen

  Neurological Surgery

  59 shared

• Kathleen R. Lamborn

  47 shared

• Jingli Wang

  Medical College of Wisconsin

  21 shared

• Tomoko Ozawa

  University of California, San Francisco

  18 shared

• Hangjun Ruan

  University of California, San Francisco

  16 shared

• Andrew W. Bollen

  University of California, San Francisco

  13 shared

• Krisztina Pongracz

  Menlo School

  9 shared

• Sergei Gryaznov

  9 shared

Similar researchers at Yale University

• Timothy Williamsonh-63
• Thomas Poggeh-58
• Stephen Darwallh-42